Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

ggmap(world) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude, color = `Ownership Type`), 
             alpha = .5, 
             size = .1) +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Starbucks locations in the world")

Most Starbucks are located in North America, so it is possible that it is an American chain.

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

tc <- get_stamenmap(
    bbox = c(left = -93.6, bottom = 44.8, right = -92.8, top = 45.1),
    maptype = "terrain",
    zoom = 11)

ggmap(tc) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 3,
             color = "#00704A") +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Starbucks locations in the Twin Cities metro area")

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

It controls how detailed the plot would be. The larger the zoom number is, the more detailed the plot is.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

tc_wc <- get_stamenmap(
    bbox = c(left = -93.6, bottom = 44.8, right = -92.8, top = 45.1), 
    maptype = "watercolor",
    zoom = 11)

ggmap(tc_wc) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 3,
             color = "#00704A") +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Starbucks locations in the Twin Cities metro area")

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

ggmap(tc) + 
  geom_point(data = Starbucks, 
             aes(x = Longitude, y = Latitude), 
             alpha = 1, 
             size = 3,
             color = "#00704A")  +
  annotate(geom = "text", x = -93.1691, y = 44.93, label = "Macalester College", color = "blue") +
  annotate(geom = "point", x = -93.1691, y = 44.9379, color = "orange") +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Starbucks locations in the Twin Cities metro area with Macalester College labeled")

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

starbucks_with_2018_pop_est <-
  starbucks_us_by_state %>% 
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>%
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000)

6. dplyr review: Look through the code above and describe what each line of code does.

We first read in the data. Because under the state column, each state has an extra dot before the name, we separate the name into a dot and the actual state name and name them accordingly. Then, the dot column is removed, and the state column is replaced with all lower case letters. In the second chunk, the census dataset is combined with the Starbuck dataset; all entries in the Starbuck dataset are kept, and entries in the census dataset with a corresponding state name in the Starbuck dataset are also kept. The last line creates a new variable representing the number of Starbucks per 10,000 population.

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

states_map <- map_data("state")

starbucks_with_2018_pop_est %>% 
  filter(state_name != "hawaii" & state_name != "alaska") %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state_name,
               fill = starbucks_per_10000)) +
  geom_point(data = Starbucks %>% 
               filter(Country == "US") %>% 
               filter(`State/Province` != "HI" & `State/Province` != "AK"),
             aes(x = Longitude, y = Latitude),
             size = .05,
             alpha = .2, 
             color = "goldenrod") +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  labs(title = "Starbucks (per 10,000 people) in the US",
       caption = "Plot created by Yunyang Zhong") +
  theme(legend.background = element_blank())

Starbucks are more common near the west coast with all the golden points and light blue states indicating the high Starbucks to population ratio.

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

• If there are other variables you want to add that could enhance your plot, do that now.

fav_yunyang <- tibble(
  place = c("Home", "Macalester College", "Shish", 
            "Nelson's Ice Cream", "Kbop Korean Bistro", "MOA",
            "MIA", "Tasty Pot", "Toppers Pizza", "Legendary Spice"),
  long = c(-93.175608887381, -93.16910346799591, -93.17061901621636,
           -93.16661014505223, -93.23685437388545, -93.24255980272581,
           -93.27349178738028, -93.23563321806229, -93.165863187381, -93.22327937388577),
  lat = c(44.940496165449325, 44.94833990834085, 44.94003346667343,
          44.9279173987216, 44.992074566055694, 44.856749781435035,
          44.958653225483964, 44.981375557267775, 44.93996737805556, 44.98333013850549),
  top = c(FALSE, TRUE, FALSE,
          FALSE, TRUE, FALSE,
          FALSE, FALSE, FALSE, TRUE),
  rank = c(4, 1, 7,
           5, 2, 9,
           10, 6, 8, 3)
  ) %>%
  arrange(rank)

pal <- colorFactor("viridis", 
                     domain = fav_yunyang$top) 

leaflet(data = fav_yunyang) %>%
  addTiles() %>%
  addCircles(lng = ~long, 
             lat = ~lat,
             label = ~place,
             color = ~pal(top),
             weight = 10,
             opacity = 1) %>% 
  addLegend(pal = pal, 
            values = ~top, 
            opacity = 0.5, 
            title = "Top 3 Favorite",
            position = "bottomright") %>% 
  addPolylines(lng = ~long, 
               lat = ~lat)

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

map <- Trips %>% 
  left_join(Stations,
            by = c("sstation" = "name")) %>% 
  filter(!is.na(long) & !is.na(lat)) %>% 
  group_by(sstation) %>% 
  mutate(total = n()) %>% 
  distinct(sstation, .keep_all = TRUE)

pal <- colorNumeric("viridis", 
                     domain = map$total) 

leaflet(data = map) %>%
  addTiles() %>%
  addCircles(lng = ~long, 
             lat = ~lat,
             label = ~sstation,
             opacity = 0.5,
             color = ~pal(total)) %>% 
  addLegend(pal = pal, 
            values = ~total, 
            opacity = 0.5, 
            title = "total number of departures",
            position = "bottomright")

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

map2 <- Trips %>% 
  left_join(Stations,
            by = c("sstation" = "name")) %>% 
  filter(!is.na(long) & !is.na(lat)) %>% 
  group_by(sstation) %>% 
  mutate(perc_casual = mean(client == "Casual")) %>% 
  distinct(sstation, .keep_all = TRUE)

pal <- colorNumeric("viridis", 
                     domain = map$perc_casual) 

leaflet(data = map2) %>%
  addTiles() %>%
  addCircles(lng = ~long, 
             lat = ~lat,
             label = ~sstation,
             color = ~pal(perc_casual),
             weight = 5, 
             opacity = 0.5) %>% 
  addLegend(pal = pal, 
            values = ~perc_casual, 
            opacity = 0.5, 
            title = "percentage of departures <br> by casual users",
            position = "bottomleft")

There seems to be more casual users in urban areas.

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

covid19 %>% 
  group_by(state, fips) %>% 
  top_n(n = 1, wt = date) %>% 
  ungroup() %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = str_to_lower(state),
               fill = cases)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "cumulative number of COVID-19 cases")

Each state has different population so simply showing the cumulative number of cases cannot tell how serious the situation is in each state.

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid19 %>% 
  mutate(state = str_to_lower(state)) %>% 
  group_by(state, fips) %>% 
  top_n(n = 1, wt = date) %>% 
  ungroup() %>% 
  left_join(census_pop_est_2018,
            by = "state") %>% 
  mutate(cases_per_10000 = cases / est_pop_2018 * 10000) %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = str_to_lower(state),
               fill = cases_per_10000)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "cumulative number of COVID-19 cases per 10,000 people")

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

covid19 %>% 
  mutate(state = str_to_lower(state)) %>% 
  filter(date == "2020-03-25" | date == "2020-07-25" | date == "2020-11-25" | date == "2021-03-25") %>% 
  left_join(census_pop_est_2018,
            by = "state") %>% 
  mutate(cases_per_10000 = cases / est_pop_2018 * 10000) %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = str_to_lower(state),
               fill = cases_per_10000)) +
  facet_wrap(~date) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  theme(legend.background = element_blank(), legend.position = "top") +
  labs(title = "cumulative number of COVID-19 cases per 10,000 people")

The cumulative number of COVID-19 cases per 10,000 people increased much faster in late 2020 and early 2021 - the color change is far more significant.

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  group_by(neighborhood) %>% 
  mutate(total_stops = n()) %>% 
  filter(problem == "suspicious") %>% 
  mutate(pro_sus = n()/total_stops) %>% 
  distinct(neighborhood, pro_sus, .keep_all = TRUE) %>% 
  arrange(desc(total_stops))

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

pal <- colorFactor("viridis", 
                     domain = MplsStops$problem) 

leaflet(data = MplsStops) %>%
  addTiles() %>%
  addCircleMarkers(lng = ~long,
                   lat = ~lat,
                   label = ~idNum,
                   color = ~pal(problem),
                   stroke = FALSE,
                   radius = 2) %>% 
  addLegend(pal = pal, 
            values = ~problem, 
            opacity = 0.5, 
            title = "problem type",
            position = "bottomright")

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_all <- mpls_nbhd %>% 
  left_join(mpls_suspicious,
            by = c("BDNAME" = "neighborhood")) %>% 
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood"))

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

pal <- colorNumeric("viridis", 
                     domain = mpls_all$pro_sus) 

leaflet(data = mpls_all) %>%
  addTiles() %>%
  addPolygons(
    stroke = FALSE, 
    fillColor = ~pal(pro_sus), 
    fillOpacity = 0.7, 
    smoothFactor = 0.5, 
    highlight = highlightOptions(weight = 5, 
                                 color = "black",
                                 fillOpacity = 0.9,
                                 bringToFront = FALSE),
    label = ~(BDNAME)) %>%
  addLegend(pal = pal, 
            values = ~pro_sus, 
            opacity = 0.5, 
            title = "proportion of suspicious stops",
            position = "bottomright")

Neighborhoods in the bottom right area have the highest proportion of stops for suspicious vehicle/person while in the middle right area the lowest.

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

mpls_all2 <- mpls_all %>%
  group_by(BDNAME) %>% 
  mutate(pro_stop = total_stops / population * 1000)

pal <- colorNumeric("viridis", 
                     domain = mpls_all2$pro_stop) 

leaflet(data = mpls_all2) %>%
  addTiles() %>%
  addPolygons(
    stroke = FALSE, 
    fillColor = ~pal(pro_stop), 
    fillOpacity = 0.7, 
    smoothFactor = 0.5, 
    highlight = highlightOptions(weight = 5, 
                                 color = "black",
                                 fillOpacity = 0.9,
                                 bringToFront = FALSE),
    label = ~(BDNAME)) %>%
  addLegend(pal = pal, 
            values = ~pro_stop, 
            opacity = 0.5, 
            title = "number of stops per <Br> 1,000 population in <Br> the neighborhood",
            position = "bottomright")

My map shows the number of stops per 1,000 population in each neighborhood - the yellower the color is, the higher the number is. Mid-City Industrial has an extremely high number stops compared to its population, following by Downtown West, Northeast Park, and Hawthorne. The rest have similar number of stops per 1,000 people.

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

github